System and method for creation, representation, and delivery of document corpus entity co-occurrence information

ABSTRACT

To respond to queries that relate to co-occurring entities on the Web, a compact sparse matrix representing entity co-occurrences is generated and then accessed to satisfy queries. The sparse matrix has groups of sub-rows, with each group corresponding to an entity in a document corpus. The groups are sorted from most occurring entity to least occurring entity. Each sub-row within a group corresponds to an entity that co-occurs in the document corpus, within a co-occurrence criterion, with the entity represented by the group, and to facilitate query response the sub-rows within a group are sorted from most occurring co-occurrence to least occurring co-occurrence.

FIELD OF THE INVENTION

The present invention relates generally to creating, representing, anddelivering entity co-occurrence information pertaining to entities in adocument corpus such as the World Wide Web.

BACKGROUND

The Internet is a ubiquitous source of information. Despite the presenceof a large number of search engines, however, all of which are designedto respond to queries for information by returning what is hoped to berelevant query responses, it remains problematic to filter throughsearch results for the answers to certain types of queries that existingsearch engines do not effectively account for. Among the types ofqueries that current search engines inadequately address are those thatrelate in general not just to a single entity, such as a single person,company, or product, but to entity combinations that are bounded byco-occurrence criteria between the entities. This is because it is oftenthe case that the co-occurrence criteria can be unnamed in the sensethat it may not be readily apparent why a particular co-occurrenceexists.

For example, consider the sentence “in their speech Sam Palmisano andSteve Mills announced a new version of IBM's database product DB2 willship by the end of third quarter.” This sentence contains the followingexample unnamed co-occurrences:

Sam Palmisano and Steve Mills, Sam Palmisano and IBM, Sam Palmisano andDB2, Steve Mills and IBM, Steve Mills and DB2.

One might wish to inquire of a large document corpus such as the Web,“which person co-occurs most often with IBM?”, but present searchengines largely cannot respond to even a simple co-occurrence query likethis one. Other co-occurrence questions with important implications butcurrently no effective answers exist, such as which medical conditionsare most often mentioned with a drug, which technologies most oftenmentioned with a company, etc. With these critical observations in mind,the invention herein is provided.

SUMMARY OF THE INVENTION

A computer is programmed to execute logic that includes receiving aquery, and in response to the query, accessing a sparse matrix thatcontains information which represents co-occurrences of entities in adocument corpus. Information obtained in the accessing act is returnedas a response to the query.

In one non-limiting implementation, the sparse matrix has groups ofsub-rows, and each group corresponds to an entity in the documentcorpus. The groups are sorted in the sparse matrix from most occurringentity to least occurring entity, with each sub-row of a groupcorresponding to an entity co-occurring in the document corpus, withinat least one co-occurrence criterion, with the entity represented by thegroup. The sub-rows within a group are sorted from most occurringco-occurrence to least occurring co-occurrence.

In the preferred non-limiting implementation, the logic can furtherinclude, in response to the query, accessing a row index that points toa starting position of a group of sub-rows in the sparse matrix. Thelogic can also include, in response to the query, accessing a headerincluding at least two bytes, the first of which indicates a fileversion and the second byte of which indicates a number of bytes usedfor at least one cardinality representing a corresponding number ofentity co-occurrences. The cardinality may be expressed exactly or usinga two-byte approximation.

If desired, the logic can also include accessing a string tableincluding an index and a corresponding data string. The index can be aconcatenated list of integers representing offsets ofentity-representing strings in the data string, and theentity-representing strings in the data string may be listed indescending order of frequency of occurrence in the document corpus.

In another aspect, a service includes receiving a query for informationcontained in the World Wide Web, and returning a response to the queryat least in part by accessing a data structure including a sparsematrix.

In yet another aspect, a method for responding to queries forinformation in a document corpus includes receiving the query and usingat least a portion of the query as an entering argument to access asparse matrix. A response to the query is returned based on the accessof the sparse matrix.

The details of the present invention, both as to its structure andoperation, can best be understood in reference to the accompanyingdrawings, in which like reference numerals refer to like parts, and inwhich:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a non-limiting computer system that canbe used to create and use the data structures shown herein to returnresponses to user queries;

FIG. 2 is a schematic representation of the present sparse matrix withrow index, along with a counterpart dense matrix representation that isshown only for illustration;

FIG. 3 is a flow chart of the logic for establishing the sparse matrix;and

FIGS. 4 and 5 show various data structures that can be used as part ofthe logic of FIG. 3.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Referring initially to FIG. 1, a system is shown, generally designated10, that includes one or more computers 12 (only a single computer 12shown in FIG. 1 for clarity of disclosure) that can communicate with acorpus 14 of documents. The corpus 14 may be the World Wide Web withcomputer-implemented Web sites, and the computer 12 can communicate withthe Web by means of a software-implemented browser 15. The computer 12includes input devices such as a keyboard 16 and/or mouse 18 or otherinput device for inputting programming data to establish the presentdata structures and/or for inputting subsequent user queries andaccessing the data structures to return responses to the queries. Thecomputer 12 can use one or more output devices 20 such as a computermonitor to display query results.

It is to be appreciated that the data structures below which facilitateco-occurrence querying can be provided to the computer 12 for executionthereof by a user of the computer so that a user can input a query andthe computer can return a response. It is to be further understood thatin other aspects, a user can access the Web or other network, input aquery to a Web server or other network server, and the server can accessthe data structures herein to return a response to the query as apaid-for service. Yet again, the data structures, owing to their compactsize, may be provided on the below-described removable portable datastorage medium and vended to users, who may purchase the portable datastorage medium and engage it with their own personal computers to queryfor co-occurrences.

The computer 12 can be, without limitation, a personal computer made byInternational Business Machines Corporation (IBM) of Armonk, N.Y. orequivalent. Other digital processors, however, may be used, such as alaptop computer, mainframe computer, palmtop computer, personalassistant, or any other suitable processing apparatus. Likewise, otherinput devices, including keypads, trackballs, and voice recognitiondevices can be used, as can other output devices, such as printers,other computers or data storage devices, and computer networks.

In any case, the computer 12 has a processor 22 that executes the logicshown herein. The logic may be implemented in software as a series ofcomputer-executable instructions. The instructions may be contained on adata storage device with a computer readable medium, such as a computerdiskette. Or, the instructions may be stored on random access memory(RAM) of the computers, on a hard disk drive, electronic read-onlymemory, optical storage device, or other appropriate data storagedevice. In an illustrative embodiment of the invention, thecomputer-executable instructions may be lines of JAVA code.

Indeed, the flow charts herein illustrate the structure of the logic ofthe present invention as embodied in computer program software. Thoseskilled in the art will appreciate that the flow charts illustrate thestructures of computer program code elements including logic circuits onan integrated circuit, that function according to this invention.Manifestly, the invention is practiced in its essential embodiment by amachine component that renders the program code elements in a form thatinstructs a digital processing apparatus (that is, a computer) toperform a sequence of function steps corresponding to those shown.

Completing the description of FIG. 1, owing to the relatively efficient,compact size (in some implementations, less than two gigabytes) of thesparse matrix and accompanying string table described herein that can beused to respond to user queries, the sparse matrix and string table maybe stored on a removable data storage media 24 such as a DVD, CD, thumbdrive, solid state portable memory device, etc.

Now referring to FIG. 2, a data structure that is generated forsearching for co-occurrences of entities in the document corpus 14 isshown and is referred to herein as an “s-web”. Essentially, in thepreferred implementation an s-web includes a header (not shown), astring table which lists the names of the entities to be considered, anda sparse matrix 30 of the co-occurrences with row index 32. As can beseen comparing the sparse matrix 30 with a corresponding dense matrixrepresentation 34, the representation of the sparse matrix drops zeroesin the dense matrix to make the resulting data structure as compact aspossible. However, the sparse matrix 30 is not merely the dense matrix34 with the zeroes dropped, but rather is a representation of the densematrix with zeroes dropped and data rearranged. Details of the sparsematrix will be discussed further below, but first the header and stringtable will be described.

First considering the header, in a preferred non-limiting implementationthe header includes two bytes, the first of which indicates the fileversion and the second of which indicates the number of bytes used forcardinalities and offsets. Smaller tables can use less bytes per entry.

As set forth further below, as used herein a “cardinality” refers to thenumber of co-occurrences between two entities. The header can indicatethe largest cardinality in the sparse matrix, either exactly or using atwo-byte approximation (reduced format) such as a 10+6 bit mantissa andorder of magnitude exponent.

The preferred non-limiting string table can have two parts, namely, anindex and the corresponding data. The index is a concatenated list ofintegers (preferably represented using the minimum number of bytes) thatprovides the offsets of the various strings. String length may becalculated by subtraction from the next occurring string.

The index of the string table is followed by the per-string data, whichlists each entity represented in the sparse matrix. The entities in thedata portion of the string table preferably are listed in descendingorder of frequency of occurrence in the document corpus 14, for reasonsthat will become clear shortly. The string data can be compressed ifdesired, but should be compressed on a per string basis, so it oftenmakes more sense to simply compress the whole file at the file systemlevel.

In generating the string table, the entities in the document corpus areobtained as set forth further below, sorted, and then concatenated toproduce the string data portion of the string table, with their offsetscalculated and recorded in the index portion. Thus, a portion of thestring table might appear as follows:

data portion: Dan SmithUSPTOIBM . . . ,

index 0 10 15 . . . , it being understood that “0” in the index pointsto just before “Dan Smith” (which starts at the zero position in thestring data), “10” in the index points to just before “USPTO” (whichstarts at the tenth position in the data string), and “15” in the indexpoints to just before “IBM” (which starts at the fifteenth position inthe data string).

Returning to the sparse matrix 30, in the preferred implementation a rowin the dense matrix, which represents a single entity, is broken intosub-rows in the sparse matrix, with each sub-row representing a columnfrom the corresponding row in the dense matrix representation. Thus, agroup of sub-rows in the sparse matrix corresponds to an entity in thedocument corpus. A column in the dense matrix representation (and hencea sub-row in the sparse matrix 30) corresponds to an entity that hassatisfied the co-occurrence criteria with the row entity as furtherdiscussed below, and the value in the column indicates the number ofco-occurrences of the two entities. Since most entities co-occur withonly a small subset of all the entities in the corpus, the dense matrixrepresentation is mostly composed of zeroes as shown. With this criticalobservation, the sparse matrix 30 is provided.

The groups of sub-rows in the sparse matrix 30 are sorted in two ways.First, the order of the groups themselves depends on the frequency ofoccurrence of the corresponding entities in the document corpus, i.e.,the first group of sub-rows correspond to the most commonly occurringentity in the document corpus 14, the second group of sub-rowsrepresents the second-most commonly occurring entity, and so on. Thismethod of sorting facilitates responding to queries such as “what is themost common cough syrup mentioned on the web?” Recall that the entitiesin the string table data portion are similarly sorted, i.e: the firststring is the most commonly occurring entity and so on.

Thus, as shown in FIG. 2, the first group of sub-rows (those beginningwith the numeral “1”) correspond to a single entity, in fact the mostfrequently occurring entity in the document corpus. To further conservespace, the first numeral of each sub-row of the sparse matrix 30 may bedropped in implementation, with the row index 32 being used to point tothe beginning of each new group of sub-rows as shown.

The second numeral in each sub-row represents a non-zero column from thedense matrix representation, and the third numeral represents the valuein the column. In the example shown in FIG. 2, there are four sub-rowsin the first group, with the first sub-row indicating that a value of“3” corresponds to column “7”, the second sub-row indicating that avalue of “2” corresponds to column “17”, the third sub-row indicatingthat a value of “1” corresponds to the first column, and the fourthsub-row indicating that a value of “1” corresponds to the thirteenthcolumn.

Accordingly, the second way in which the sparse matrix 30 is sorted maynow be appreciated. Not only are the groups of sub-rows intersorted byfrequency of occurrence of the corresponding entities, but within eachgroup, the sub-rows are intrasorted by cardinality, with the sub-rowindicating the highest number of co-occurrences first, the sub-rowindicating the second-highest number of co-occurrences second, and soon. This second way in which the sparse matrix 30 is sorted thusfacilitates responding to queries such as “which cough syrups are mostoften co-mentioned with aspirin?”

FIGS. 3-5 illustrate how the data structures discussed above can begenerated. Commencing at block 40, a hierarchical structure of entityclasses may be established. More specifically, consider that entitiescan be regarded as annotations which have been placed on a documenteither manually or automatically via an algorithm. In a non-limitingimplementation each entity can be an unstructured information managementarchitecture (UIMA) annotation which records the unique identifier ofthe entity, its location on the document, and the number of tokens bywhich the entity is represented. This information is then compiled intoa vector of annotations per document as set forth further below. Block40 recognizes that many annotations fall into classes of annotation, andentities are no different. In the example in the background, “SamPalmisano” and “Steve Mills” are both of the “People” class of entities,whereas the annotation “IBM” is of the Organization class and “DB2” canbe considered part of the Product class of entities. This non-limitingillustrative classification allows for a simple hierarchical structureof entities to be created:

/Entity/People/Sam Palmisano

/Entity/People/Steve Mills

/Entity/Organizations/IBM

/Entity/Products/DB2

When annotations are classified and structured in this manner, the logiccan move to block 42 to examine each document (or a relevant subsetthereof) in the corpus and determine entities, their locations, and thenumber of tokens associated with each entity to thereby establishannotation vectors. Multiple annotations may be produced at a givenannotation location, e.g., at the location in a document of “SamPalmisano”, annotations for “Entity”, “Entity/People”, and“Entity/People/Sam Palmisano” can be produced.

FIG. 4 illustrates how annotation vectors are generated. While theexample documents in FIG. 4 are in Web markup language, the invention isnot limited to a particular format of document.

As shown, a raw document 44 with document ID, content, and other dataknown to those of skill in the art (crawl date, URL, etc.) can be storedat 46 and then operated on by an annotator 48 to produce an annotateddocument 50, which lists, among things, various entities in the documentas shown. The annotated document 50 may also be stored at 46. An indexcomponent 52 then accesses the annotated documents 50 to produceannotation vectors 54, showing, for each entity, the documents in whichit appears.

Proceeding to block 56 in FIG. 3, the annotation vectors are inverted bya software-implemented indexer such that for each document, a table ofunique annotations is produced and the locations on the document wherethe annotation occurred are recorded. Within a non-limiting indexer, thelocation, span and unique entity identifiers are recorded for eachlocation. When a given annotation has occurred more than once on adocument, the annotation locations are structured as a list ofannotations, sorted by the order the individual annotations occurred inthe document. If an annotation is unique on a document, the table can beconsidered to point at a location list with a size of one.

Briefly referencing FIG. 5, as more documents are processed by theindexer, a unique annotation table 58 (referred to herein a dictionary)and the corresponding annotation lists are merged to produce thedocument table 60. Once all documents have been processed a final indexas shown in FIG. 5 is produced which contains all the unique annotationsand lists of the documents in which they have occurred, also preferablywith the location within a document of each occurrence. The datastructure of FIG. 5 facilitates efficient entity (term) lookup,efficient Boolean operations, and efficient storage of a large number ofdata records.

Returning once again to FIG. 3, the logic next moves to block 62 todefine a set of inner entities and a set of outer entities. Notionally,the inner entities define the sub-row groups and the outer entitiesdefine the sub-rows within a group in the sparse matrix 30 of FIG. 2.

Thus, the inner set is the class of entities of primary interest. Theinner set can be the set of all entities, or a subset of all entities.The outer set is the class of entities of interest for determining if arelationship exists between that entity and an inner entity, and thisset may also be the set of all entities or only a subset thereof.

Once the classes of entities are defined, the lists of documentlocations for those classes are retrieved from the indexer, i.e., thedata structures of FIGS. 4 and 5 are accessed. At block 64 the lists arescanned sequentially to determine all the pairs of inner and outerentities which occur within a given proximity boundary. Proximityboundaries can be within the same sentence, paragraph, document, orwithin a fixed number of tokens.

When a pair is determined to be within the proximity constraint, atblock 66 a loop is entered in which the unique entity identifiers storedwithin the two locations are compared to each other at decision diamond68 to ensure that the entities are unique. If they are the same, theprocess accesses the next pair (assuming the Do loop is not complete) atblock 70 and loops back to decision diamond 68. On the other hand, ifthe entities are unique from each other the pair is appended to a listof all pairs which have been discovered at block 72.

Once the lists of locations have been exhausted (i.e., the DO loop iscomplete), the list of pairs is processed at block 74 to produce a tableof all unique pairs which occurred and the number of times the pairoccurred. This table is sorted in accordance with principles discussedabove into the sparse matrix 30 of FIG. 2. The string table is likewiseproduced using the lists in FIGS. 4 and 5.

To execute a query, the sparse matrix 30 and string table may be used asfollows. It is to be understood that other sparse matrices lesspreferably may be used, but in the preferred implementation the sparsematrix 30, advantageously ordered as discussed above, is used.

For an example query “which “N” medical conditions are most oftenmentioned with drug X?”, the string table (which, recall, has the sameorder of entities as the sparse matrix) is accessed to locate the drug X(and hence the position of its group of sub-rows in the sparse matrix).Then the sparse matrix is accessed using the drug entity as enteringargument, and the column represented by the highest sub-row in the groupcorresponding to a medical condition is retrieved. Since the sub-rowsare in order of cardinality, the first sub-row indicates the entity inthe corpus having the most co-occurrences with the drug X, and it isexamined to determine whether it corresponds to a co-occurring entitythat is classified as a “condition”. If not, the next sub-row isexamined, and so on, until the highest cardinality “N” sub-rowsindicating the most frequently co-occurring conditions are identified.The result is then returned. For a simpler query, e.g., “which drug ismost often mentioned on the Web”, the string table is accessed from thebeginning to find the highest cardinality entity that has beenclassified as a drug, and the result returned.

An s-web of around thirty thousand co-occurrence entries may be smallerthan two gigabytes. This means that these “co-occurrence snapshots” canfit easily on removable media (DVD, CD, thumb drive, etc). Applicationscan be included on this media as well, allowing stand alone delivery ofthese facts which customers can explore to discover actionable businessinsights.

While the particular SYSTEM AND METHOD FOR CREATION, REPRESENTATION, ANDDELIVERY OF DOCUMENT CORPUS ENTITY CO-OCCURRENCE INFORMATION is hereinshown and described in detail, it is to be understood that the subjectmatter which is encompassed by the present invention is limited only bythe claims.

1. A computer implementing a method for responding to queries for information in a document corpus, the method implemented by the computer comprising: receiving the query; using at least a portion of the query as an entering argument to access a string table having an index and corresponding data, the index being a concatenated list of integers providing offsets of various character strings, the index being followed by per-string data listing each entity represented in a sparse matrix, the entities in the string table being listed in order of frequency of occurrence in a document corpus, one and only one person entity being entered in the matrix even if a person establishing the entity is referred to by plural phrases different from each other in the corpus; using a selection from the string table selected in response to the portion of the query to access a sparse matrix; and returning a response to the query at least in part based on the access of the sparse matrix, wherein the document corpus includes World Wide Web pages and the sparse matrix includes entity representations that are respective groups of sub-rows in the sparse matrix, wherein the groups are sorted relative to each other from most occurring entity to least occurring entity, with each sub-row of a group corresponding to an entity co-occurring in the document corpus with the entity represented by the group, and furthermore wherein the sub-rows within a group of sub-rows are internally sorted independently of other groups of sub-rows based on frequencies of co-occurrences within the group, from a first a sub-row indicating a highest number of co-occurrences to a last sub-row indicating a lowest number of co-occurrences. 